Codexis (CDXS) Operating Expenses (2010 - 2026)
Codexis (CDXS) recorded Operating Expenses of $26.14 million in Q2 2026, down 7.3% from $28.19 million a year earlier but up 12.2% from the prior quarter.
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Analysis
Codexis (CDXS) Operating Expenses (2010 - 2026) Analysis & Trends
On a TTM basis, Codexis' Operating Expenses came in at $105.34 million as of Jun 30, 2026, down 7.9% year-over-year; for FY2025, it came in at $112.13 million, down 4.9% from FY2024.
- Annual Operating Expenses has declined for three straight years, though with a five-year compound annual growth rate of 3.8% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $117.86 million in FY2024 (-14.7%), $138.21 million in FY2023 (-20.3%), $173.47 million in FY2022 (+36.1%) and $127.45 million in FY2021 (+37.1%).
- Quarterly Operating Expenses has ranged from $23.29 million in Q1 2026 to $45.11 million in Q3 2022 over the past five years.
- On a year-over-year basis, Operating Expenses has declined for six consecutive quarters, with an average decline of 7.8% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 60.8% in Q1 2022, against a decline of 39.8% in Q4 2023 at the low end.
- Per Business Quant, the preceding three quarters came in at $23.29 million (Q1 2026), $28.36 million (Q4 2025) and $27.55 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 254.87 Bn | 233.89 Bn | 4.88 Bn | 9.91 Bn |
| 2 | Abbott Laboratories | 178.85 Bn | 149.28 Bn | 7.27 Bn | 10.90 Bn |
| 3 | Danaher | 158.49 Bn | 142.30 Bn | 3.61 Bn | 2.48 Bn |
| 4 | Intuitive Surgical | 154.99 Bn | 134.41 Bn | 1.96 Bn | 988.50 Mn |
| 5 | Medtronic | 121.02 Bn | 86.28 Bn | 6.34 Bn | 4.06 Bn |
| 6 | Stryker | 116.94 Bn | 103.05 Bn | 4.50 Bn | 2.84 Bn |
| 7 | Boston Scientific | 66.98 Bn | 61.75 Bn | 3.85 Bn | 2.67 Bn |
| 8 | Becton Dickinson | 53.23 Bn | 50.38 Bn | 2.32 Bn | 4.32 Bn |
| 9 | Edwards Lifesciences | 50.83 Bn | 34.83 Bn | 1.35 Bn | 840.10 Mn |
| 10 | Codexis | 112.00 Mn | -145.05 Mn | 11.40 Mn | 26.14 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 26.14 Mn |
| Mar 31, 2026 | 23.29 Mn |
| Dec 31, 2025 | 28.36 Mn |
| Sep 30, 2025 | 27.55 Mn |
| Jun 30, 2025 | 28.19 Mn |
| Mar 31, 2025 | 28.03 Mn |
| Dec 31, 2024 | 28.80 Mn |
| Sep 30, 2024 | 29.39 Mn |
| Jun 30, 2024 | 30.71 Mn |
| Mar 31, 2024 | 28.96 Mn |
| Dec 31, 2023 | 26.28 Mn |
| Sep 30, 2023 | 41.34 Mn |
| Jun 30, 2023 | 33.95 Mn |
| Mar 31, 2023 | 36.65 Mn |
| Dec 31, 2022 | 43.63 Mn |
| Sep 30, 2022 | 45.11 Mn |
| Jun 30, 2022 | 41.02 Mn |
| Mar 31, 2022 | 43.73 Mn |
| Dec 31, 2021 | 34.89 Mn |
| Sep 30, 2021 | 35.44 Mn |
API Access
Codexis Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=CDXS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "CDXS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=CDXS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();